多机器人协同在复杂环境里边探索边追踪目标，精度高且不遗漏。
Distributed Multi-Robot Multi-Target Simultaneous Search and Tracking in an Unknown Non-convex Environment
- 融合三种策略：前沿探索、全覆盖规划、传感器追踪
- 仿真验证优于传统方法，兼顾覆盖与精准追踪
- 适合搜救、监测等复杂未知场景的多机器人系统
在室内和地下等未知非凸环境中，部署机器人集群同时执行环境探索、目标搜索与高精度跟踪，对环境监测和救援任务至关重要。现有研究虽在环境探索、信息搜索和目标追踪方面取得进展，但尚未建立能同步优化三项任务的统一框架。本文提出一种新型运动规划算法框架，集成前沿探索策略、基于Lloyd算法的保证全覆盖策略以及基于传感器的多目标追踪策略。该框架在探索过程中实现覆盖率与高精度主动追踪的平衡。通过一系列MATLAB仿真验证，结果表明该方法有效性显著，优于标准方法。
原文摘要 · Abstract (English)
In unknown non-convex environments, such as indoor and underground spaces, deploying a fleet of robots to explore the surroundings while simultaneously searching for and tracking targets of interest to maintain high-precision data collection represents a fundamental challenge that urgently requires resolution in applications such as environmental monitoring and rescue operations. Current research has made significant progress in addressing environmental exploration, information search, and target tracking problems, but has yet to establish a framework for simultaneously optimizing these tasks in complex environments. In this paper, we propose a novel motion planning algorithm framework that integrates three control strategies: a frontier-based exploration strategy, a guaranteed coverage strategy based on Lloyd's algorithm, and a sensor-based multi-target tracking strategy. By incorporating these three strategies, the proposed algorithm balances coverage search and high-precision active tracking during exploration. Our approach is validated through a series of MATLAB simulations, demonstrating validity and superiority over standard approaches.
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